Adversarial training is widely adopted to proactively make the ML classifiers aware of the evasion samples. However, extending the capability of \(\mathcal {D}\)  of a GAN from discriminating between real and fake samples to differentiating between normal and anomaly data renders the adversarial training needless. Hence \(\mathcal {D}\)  can be used as an evasion-aware classifier [1]}, [2]}. In [evagan], authors propose EVAGAN that provides such type of \(\mathcal {D}\)  and compare its performance with the \(\mathcal {D}\)  of ACGAN and other ML classifiers like xgboost (XGB), naive bayes (NB), decision tree (DT), random forests (RF), k-nearest neighbours (KNN) and logistic regression (LR). EVAGAN's \(\mathcal {D}\)  outperforms the ML classifiers in black box testing and gives 100% accuracy in normal and evasion samples estimation. However, EVAGAN, like other GAN models, is agnostic of semantics and functionality preservation of malicious synthetic samples. In this paper, we propose a novel type of GAN based on EVAGAN leveraged with deep reinforcement learning to address the problem of functionality preservation.
